{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\PSRM McDonald Hanmer.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Aug 2023, 19:04:38

{com}. do "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\McDonald Hanmer PSRM Replication Script.do"
{txt}
{com}. *Article Name: Evaluating Methods for Examining the Relative Persuasiveness of Policy Arguments
. *Journal: Political Science Research and Methods
. *Evaluating Methods for Examining the Relative Persuasiveness of Policy Arguments
. 
. ********************
. ******Figure 1******
. ********************
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. *Post Only (Generates values for left two bars in Figure 1)
. reg experiment1outcome dc_taxesonly if dc_corruptiononly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       792
{txt}{hline 13}{c +}{hline 34}   F(1, 790)       = {res}     2.59
{txt}       Model {c |} {res} .629264585         1  .629264585   {txt}Prob > F        ={res}    0.1077
{txt}    Residual {c |} {res} 191.702806       790   .24266178   {txt}R-squared       ={res}    0.0033
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0020
{txt}       Total {c |} {res} 192.332071       791  .243150532   {txt}Root MSE        =   {res} .49261

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}experimen~me{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dc_taxesonly {c |}{col 14}{res}{space 2} .0563776{col 26}{space 2} .0350099{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0123458{col 67}{space 3} .1251009
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}    .3875{col 26}{space 2} .0246304{col 37}{space 1}   15.73{col 46}{space 3}0.000{col 54}{space 4} .3391513{col 67}{space 3} .4358487
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg experiment1outcome dc_corruptiononly if dc_taxesonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       791
{txt}{hline 13}{c +}{hline 34}   F(1, 789)       = {res}     0.40
{txt}       Model {c |} {res} .093717566         1  .093717566   {txt}Prob > F        ={res}    0.5281
{txt}    Residual {c |} {res} 185.638267       789  .235282975   {txt}R-squared       ={res}    0.0005
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0008
{txt}       Total {c |} {res} 185.731985       790  .235103778   {txt}Root MSE        =   {res} .48506

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}experiment1outc~e{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dc_corruptiononly {c |}{col 19}{res}{space 2}-.0217711{col 31}{space 2} .0344957{col 42}{space 1}   -0.63{col 51}{space 3}0.528{col 59}{space 4}-.0894853{col 72}{space 3} .0459431
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}    .3875{col 31}{space 2}  .024253{col 42}{space 1}   15.98{col 51}{space 3}0.000{col 59}{space 4}  .339892{col 72}{space 3}  .435108
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *RMWS (Generates values for right two bars in Figure 1)
. ttest experiment2outcomebaseline=experiment2outcometaxes

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
exper~ne {c |}{res}{col 12}    786{col 22} .3905852{col 34} .0174132{col 46} .4881922{col 58} .3564032{col 70} .4247673
{txt}experi~s {c |}{res}{col 12}    786{col 22} .4860051{col 34} .0178388{col 46} .5001223{col 58} .4509877{col 70} .5210224
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    786{col 22}-.0954198{col 34} .0156874{col 46} .4398082{col 58}-.1262141{col 70}-.0646256
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}experiment2out~e{txt} - {res}experiment2out~s{txt})       t = {res} -6.0826
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     785

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. ttest experiment2outcomebaseline=experiment2outcomecorruption

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
exper~ne {c |}{res}{col 12}    786{col 22} .3905852{col 34} .0174132{col 46} .4881922{col 58} .3564032{col 70} .4247673
{txt}experi~n {c |}{res}{col 12}    786{col 22} .2989822{col 34}   .01634{col 46} .4581035{col 58} .2669069{col 70} .3310575
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    786{col 22} .0916031{col 34} .0160733{col 46} .4506253{col 58} .0600514{col 70} .1231547
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}experiment2out~e{txt} - {res}experiment2out~n{txt})       t = {res}  5.6991
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     785

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. ********************
. ******Figure 2******
. ********************
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *Post Only (Generates values for left two bars in Figure 2)
. reg betweensubjectsdv drug_saveonly if drug_limitonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       777
{txt}{hline 13}{c +}{hline 34}   F(1, 775)       = {res}     1.89
{txt}       Model {c |} {res} .107330033         1  .107330033   {txt}Prob > F        ={res}    0.1698
{txt}    Residual {c |} {res} 44.0496841       775  .056838302   {txt}R-squared       ={res}    0.0024
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0011
{txt}       Total {c |} {res} 44.1570142       776  .056903369   {txt}Root MSE        =   {res} .23841

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubj~v{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_saveonly {c |}{col 15}{res}{space 2}-.0235248{col 27}{space 2} .0171193{col 38}{space 1}   -1.37{col 47}{space 3}0.170{col 55}{space 4}-.0571305{col 68}{space 3} .0100809
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9517426{col 27}{space 2} .0123443{col 38}{space 1}   77.10{col 47}{space 3}0.000{col 55}{space 4} .9275104{col 68}{space 3} .9759748
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg betweensubjectsdv drug_limitonly if drug_saveonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       803
{txt}{hline 13}{c +}{hline 34}   F(1, 801)       = {res}    82.24
{txt}       Model {c |} {res}   10.64094         1    10.64094   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 103.642995       801  .129392004   {txt}R-squared       ={res}    0.0931
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0920
{txt}       Total {c |} {res} 114.283935       802  .142498672   {txt}Root MSE        =   {res} .35971

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2308124{col 28}{space 2} .0254521{col 39}{space 1}   -9.07{col 48}{space 3}0.000{col 56}{space 4} -.280773{col 69}{space 3}-.1808518
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9517426{col 28}{space 2} .0186251{col 39}{space 1}   51.10{col 48}{space 3}0.000{col 56}{space 4} .9151828{col 69}{space 3} .9883025
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *RMWS (Generates values for right two bars in Figure 2)
. 
. ttest within_initial=withinsubjectssaveoutcome

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    794{col 22} .9345088{col 34} .0087851{col 46} .2475465{col 58}  .917264{col 70} .9517536
{txt}w~save~e {c |}{res}{col 12}    794{col 22} .9118388{col 34} .0100684{col 46} .2837079{col 58} .8920749{col 70} .9316027
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    794{col 22}   .02267{col 34} .0106631{col 46} .3004661{col 58} .0017387{col 70} .0436013
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~eoutcome{txt})         t = {res}  2.1260
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     793

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}0.9831         {txt}Pr(|T| > |t|) = {res}0.0338          {txt}Pr(T > t) = {res}0.0169
{txt}
{com}. ttest within_initial=withinsubjectslimitoutcome

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    794{col 22} .9345088{col 34} .0087851{col 46} .2475465{col 58}  .917264{col 70} .9517536
{txt}w~limi~e {c |}{res}{col 12}    794{col 22} .4219144{col 34} .0175377{col 46} .4941763{col 58} .3874886{col 70} .4563401
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    794{col 22} .5125945{col 34}  .018793{col 46} .5295482{col 58} .4757046{col 70} .5494843
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~toutcome{txt})         t = {res} 27.2759
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     793

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. 
. 
. ********************
. ******APPENDIX******
. ********************
. 
. **********
. *Table A1*
. **********
. 
. *Study 1
. *Lines 46-52 produce values for the first column of Table A1
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. tab Party 

      {txt}Party {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        670       34.03       34.03
{txt}Independent {c |}{res}        656       33.32       67.34
{txt}      Other {c |}{res}         53        2.69       70.04
{txt} Republican {c |}{res}        590       29.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,969      100.00
{txt}
{com}. tab ideo3

   {txt}RECODE of {c |}
IdeologyNum2 {c |}
  (Ideology, {c |}
     recodes {c |}
     haven't {c |}
     thought {c |}
 about it as {c |}
  moderates) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Liberal {c |}{res}        494       25.09       25.09
{txt}Conservative {c |}{res}        666       33.82       58.91
{txt}    Moderate {c |}{res}        809       41.09      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,969      100.00
{txt}
{com}. tab Race

                            {txt}Race {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
         Asian or Asian American {c |}{res}        108        5.49        5.49
{txt}       Black or African American {c |}{res}        378       19.21       24.70
{txt}Native American or Alaska Native {c |}{res}         45        2.29       26.98
{txt}                           Other {c |}{res}         53        2.69       29.67
{txt}              White or Caucasian {c |}{res}      1,384       70.33      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      1,968      100.00
{txt}
{com}. tab male

     {txt}1=Male; {c |}
      0=else {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Female/Other {c |}{res}      1,006       51.09       51.09
{txt}        Male {c |}{res}        963       48.91      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,969      100.00
{txt}
{com}. tab educ3

  {txt}RECODE of {c |}
 EducRecode {c |}
   (Ordinal {c |}
  Education {c |}
     Level) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   hsorless {c |}{res}        453       23.01       23.01
{txt}somecollege {c |}{res}        728       36.97       59.98
{txt}   college+ {c |}{res}        788       40.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,969      100.00
{txt}
{com}. 
. *Study 2
. *Lines 56-63 produce values for the first column of Table A1
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. tab pidstem

  {txt}Generally {c |}
  speaking, {c |}
     do you {c |}
    usually {c |}
   think of {c |}
yourself as {c |}
    a ....? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        770       38.46       38.46
{txt} Republican {c |}{res}        567       28.32       66.78
{txt}Independent {c |}{res}        564       28.17       94.96
{txt}      Other {c |}{res}        101        5.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,002      100.00
{txt}
{com}. tab ideo3

   {txt}RECODE of {c |}
ideology (We {c |}
  hear a lot {c |}
     of talk {c |}
  these days {c |}
       about {c |}
liberals and {c |}
    conserva {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
     Liberal {c |}{res}        507       25.32       25.32
{txt}Conservative {c |}{res}        660       32.97       58.29
{txt}    Moderate {c |}{res}        835       41.71      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      2,002      100.00
{txt}
{com}. tab white

  {txt}RECODE of {c |}
  ethnicity {c |}
(ethnicity) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        576       28.17       28.17
{txt}          1 {c |}{res}      1,469       71.83      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,045      100.00
{txt}
{com}. tab black

  {txt}RECODE of {c |}
  ethnicity {c |}
(ethnicity) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,774       86.75       86.75
{txt}          1 {c |}{res}        271       13.25      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,045      100.00
{txt}
{com}. tab male

  {txt}RECODE of {c |}
     gender {c |}
   (gender) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,053       51.49       51.49
{txt}          1 {c |}{res}        992       48.51      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,045      100.00
{txt}
{com}. tab educ3

  {txt}RECODE of {c |}
  education {c |}
   (What is {c |}
the highest {c |}
   level of {c |}
 school you {c |}
       have {c |}
  completed {c |}
       or t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   hsorless {c |}{res}        654       32.67       32.67
{txt}somecollege {c |}{res}        742       37.06       69.73
{txt}   college+ {c |}{res}        606       30.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,002      100.00
{txt}
{com}. 
. ***********
. *Table A2a*
. ***********
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. tab pidwoleaners dcstatehood_cond , col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

RECODE of party7pt {c |}                    dcstatehood_cond
     (7-point PID) {c |}   Control  Taxes Onl  Corr Only  RMWS: T-C   RMWS C-T {c |}     Total
{hline 19}{c +}{hline 55}{c +}{hline 10}
          Democrat {c |}{res}       138        129        130        143        130 {txt}{c |}{res}       670 
                   {txt}{c |}{res}     34.50      32.91      33.25      36.29      33.16 {txt}{c |}{res}     34.03 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
               GOP {c |}{res}       114        116        112        119        129 {txt}{c |}{res}       590 
                   {txt}{c |}{res}     28.50      29.59      28.64      30.20      32.91 {txt}{c |}{res}     29.96 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
Independent/Leaner {c |}{res}       148        147        149        132        133 {txt}{c |}{res}       709 
                   {txt}{c |}{res}     37.00      37.50      38.11      33.50      33.93 {txt}{c |}{res}     36.01 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
             Total {c |}{res}       400        392        391        394        392 {txt}{c |}{res}     1,969 
                   {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab ideo3 dcstatehood_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

   RECODE of {c |}
IdeologyNum2 {c |}
  (Ideology, {c |}
     recodes {c |}
     haven't {c |}
     thought {c |}
 about it as {c |}                    dcstatehood_cond
  moderates) {c |}   Control  Taxes Onl  Corr Only  RMWS: T-C   RMWS C-T {c |}     Total
{hline 13}{c +}{hline 55}{c +}{hline 10}
     Liberal {c |}{res}       111        103        102         94         84 {txt}{c |}{res}       494 
             {txt}{c |}{res}     27.75      26.28      26.09      23.86      21.43 {txt}{c |}{res}     25.09 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
Conservative {c |}{res}       129        131        128        136        142 {txt}{c |}{res}       666 
             {txt}{c |}{res}     32.25      33.42      32.74      34.52      36.22 {txt}{c |}{res}     33.82 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
    Moderate {c |}{res}       160        158        161        164        166 {txt}{c |}{res}       809 
             {txt}{c |}{res}     40.00      40.31      41.18      41.62      42.35 {txt}{c |}{res}     41.09 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
       Total {c |}{res}       400        392        391        394        392 {txt}{c |}{res}     1,969 
             {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab Race dcstatehood_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

                      {c |}                    dcstatehood_cond
                 Race {c |}   Control  Taxes Onl  Corr Only  RMWS: T-C   RMWS C-T {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
Asian or Asian Amer.. {c |}{res}        25         28         19         16         20 {txt}{c |}{res}       108 
                      {txt}{c |}{res}      6.25       7.14       4.86       4.06       5.12 {txt}{c |}{res}      5.49 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
Black or African Am.. {c |}{res}        82         60         79         78         79 {txt}{c |}{res}       378 
                      {txt}{c |}{res}     20.50      15.31      20.20      19.80      20.20 {txt}{c |}{res}     19.21 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
Native American or .. {c |}{res}         9         15          5         10          6 {txt}{c |}{res}        45 
                      {txt}{c |}{res}      2.25       3.83       1.28       2.54       1.53 {txt}{c |}{res}      2.29 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Other {c |}{res}         9         13         15         10          6 {txt}{c |}{res}        53 
                      {txt}{c |}{res}      2.25       3.32       3.84       2.54       1.53 {txt}{c |}{res}      2.69 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
   White or Caucasian {c |}{res}       275        276        273        280        280 {txt}{c |}{res}     1,384 
                      {txt}{c |}{res}     68.75      70.41      69.82      71.07      71.61 {txt}{c |}{res}     70.33 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}       400        392        391        394        391 {txt}{c |}{res}     1,968 
                      {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab male dcstatehood_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     1=Male; {c |}                    dcstatehood_cond
      0=else {c |}   Control  Taxes Onl  Corr Only  RMWS: T-C   RMWS C-T {c |}     Total
{hline 13}{c +}{hline 55}{c +}{hline 10}
Female/Other {c |}{res}       197        203        199        198        209 {txt}{c |}{res}     1,006 
             {txt}{c |}{res}     49.25      51.79      50.90      50.25      53.32 {txt}{c |}{res}     51.09 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
        Male {c |}{res}       203        189        192        196        183 {txt}{c |}{res}       963 
             {txt}{c |}{res}     50.75      48.21      49.10      49.75      46.68 {txt}{c |}{res}     48.91 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
       Total {c |}{res}       400        392        391        394        392 {txt}{c |}{res}     1,969 
             {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab educ3 dcstatehood_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  RECODE of {c |}
 EducRecode {c |}
   (Ordinal {c |}
  Education {c |}                    dcstatehood_cond
     Level) {c |}   Control  Taxes Onl  Corr Only  RMWS: T-C   RMWS C-T {c |}     Total
{hline 12}{c +}{hline 55}{c +}{hline 10}
   hsorless {c |}{res}        86         89         93        100         85 {txt}{c |}{res}       453 
            {txt}{c |}{res}     21.50      22.70      23.79      25.38      21.68 {txt}{c |}{res}     23.01 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
somecollege {c |}{res}       148        150        152        128        150 {txt}{c |}{res}       728 
            {txt}{c |}{res}     37.00      38.27      38.87      32.49      38.27 {txt}{c |}{res}     36.97 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
   college+ {c |}{res}       166        153        146        166        157 {txt}{c |}{res}       788 
            {txt}{c |}{res}     41.50      39.03      37.34      42.13      40.05 {txt}{c |}{res}     40.02 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
      Total {c |}{res}       400        392        391        394        392 {txt}{c |}{res}     1,969 
            {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. 
. ***********
. *Table A2b*
. ***********
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. tab pidwoleaners drug_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  RECODE of {c |}
    pidstem {c |}
 (Generally {c |}
  speaking, {c |}
     do you {c |}
    usually {c |}
   think of {c |}
yourself as {c |}                       drug_cond
      a ... {c |}   Control  Save Mon   Lim Acc O  RMWS: S-L   RMWS L-S {c |}     Total
{hline 12}{c +}{hline 55}{c +}{hline 10}
   Democrat {c |}{res}       161        139        155        169        146 {txt}{c |}{res}       770 
            {txt}{c |}{res}     43.16      34.41      36.05      41.42      37.73 {txt}{c |}{res}     38.46 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
 Republican {c |}{res}        98        132        122        111        104 {txt}{c |}{res}       567 
            {txt}{c |}{res}     26.27      32.67      28.37      27.21      26.87 {txt}{c |}{res}     28.32 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
Independent {c |}{res}       114        133        153        128        137 {txt}{c |}{res}       665 
            {txt}{c |}{res}     30.56      32.92      35.58      31.37      35.40 {txt}{c |}{res}     33.22 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
      Total {c |}{res}       373        404        430        408        387 {txt}{c |}{res}     2,002 
            {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab ideo3 drug_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

   RECODE of {c |}
ideology (We {c |}
  hear a lot {c |}
     of talk {c |}
  these days {c |}
       about {c |}
liberals and {c |}                       drug_cond
    conserva {c |}   Control  Save Mon   Lim Acc O  RMWS: S-L   RMWS L-S {c |}     Total
{hline 13}{c +}{hline 55}{c +}{hline 10}
     Liberal {c |}{res}       109         99         92        112         95 {txt}{c |}{res}       507 
             {txt}{c |}{res}     29.22      24.50      21.40      27.45      24.55 {txt}{c |}{res}     25.32 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
Conservative {c |}{res}        96        151        143        135        135 {txt}{c |}{res}       660 
             {txt}{c |}{res}     25.74      37.38      33.26      33.09      34.88 {txt}{c |}{res}     32.97 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
    Moderate {c |}{res}       168        154        195        161        157 {txt}{c |}{res}       835 
             {txt}{c |}{res}     45.04      38.12      45.35      39.46      40.57 {txt}{c |}{res}     41.71 
{txt}{hline 13}{c +}{hline 55}{c +}{hline 10}
       Total {c |}{res}       373        404        430        408        387 {txt}{c |}{res}     2,002 
             {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab white drug_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 RECODE of {c |}
 ethnicity {c |}
(ethnicity {c |}                       drug_cond
         ) {c |}   Control  Save Mon   Lim Acc O  RMWS: S-L   RMWS L-S {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}       101        110        115        129        121 {txt}{c |}{res}       576 
           {txt}{c |}{res}     26.65      26.83      25.90      30.86      30.71 {txt}{c |}{res}     28.17 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
         1 {c |}{res}       278        300        329        289        273 {txt}{c |}{res}     1,469 
           {txt}{c |}{res}     73.35      73.17      74.10      69.14      69.29 {txt}{c |}{res}     71.83 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       379        410        444        418        394 {txt}{c |}{res}     2,045 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab black drug_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 RECODE of {c |}
 ethnicity {c |}
(ethnicity {c |}                       drug_cond
         ) {c |}   Control  Save Mon   Lim Acc O  RMWS: S-L   RMWS L-S {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}       330        356        387        360        341 {txt}{c |}{res}     1,774 
           {txt}{c |}{res}     87.07      86.83      87.16      86.12      86.55 {txt}{c |}{res}     86.75 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
         1 {c |}{res}        49         54         57         58         53 {txt}{c |}{res}       271 
           {txt}{c |}{res}     12.93      13.17      12.84      13.88      13.45 {txt}{c |}{res}     13.25 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       379        410        444        418        394 {txt}{c |}{res}     2,045 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab male drug_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 RECODE of {c |}
    gender {c |}                       drug_cond
  (gender) {c |}   Control  Save Mon   Lim Acc O  RMWS: S-L   RMWS L-S {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}       194        207        249        206        197 {txt}{c |}{res}     1,053 
           {txt}{c |}{res}     51.19      50.49      56.08      49.28      50.00 {txt}{c |}{res}     51.49 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
         1 {c |}{res}       185        203        195        212        197 {txt}{c |}{res}       992 
           {txt}{c |}{res}     48.81      49.51      43.92      50.72      50.00 {txt}{c |}{res}     48.51 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       379        410        444        418        394 {txt}{c |}{res}     2,045 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab educ3 drug_cond, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  RECODE of {c |}
  education {c |}
   (What is {c |}
the highest {c |}
   level of {c |}
 school you {c |}
       have {c |}
  completed {c |}                       drug_cond
       or t {c |}   Control  Save Mon   Lim Acc O  RMWS: S-L   RMWS L-S {c |}     Total
{hline 12}{c +}{hline 55}{c +}{hline 10}
   hsorless {c |}{res}       105        137        150        139        123 {txt}{c |}{res}       654 
            {txt}{c |}{res}     28.15      33.91      34.88      34.07      31.78 {txt}{c |}{res}     32.67 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
somecollege {c |}{res}       155        138        162        135        152 {txt}{c |}{res}       742 
            {txt}{c |}{res}     41.55      34.16      37.67      33.09      39.28 {txt}{c |}{res}     37.06 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
   college+ {c |}{res}       113        129        118        134        112 {txt}{c |}{res}       606 
            {txt}{c |}{res}     30.29      31.93      27.44      32.84      28.94 {txt}{c |}{res}     30.27 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
      Total {c |}{res}       373        404        430        408        387 {txt}{c |}{res}     2,002 
            {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. 
. ***********
. *Figure A1*
. ***********
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. *Post Only (Produces values for the left three bars in Figure A1)
. tabstat experiment1outcome if dc_control==1, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:experimen~me} {...}
{c |}{...}
 {res}      400     .3875  .0243895  1.258812
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat experiment1outcome if dc_taxesonly==1, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:experimen~me} {...}
{c |}{...}
 {res}      392  .4438776  .0251263  1.120749
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat experiment1outcome if dc_corruptiononly==1, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:experimen~me} {...}
{c |}{...}
 {res}      391  .3657289  .0243885  1.318603
{txt}{hline 13}{c BT}{hline 40}

{com}. 
. *RMWS (Produces values for the right three bars in Figure A1)
. tabstat experiment2outcomebaseline, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:experimen~ne} {...}
{c |}{...}
 {res}      786  .3905852  .0174132  1.249899
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat experiment2outcometaxes, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:experiment~s} {...}
{c |}{...}
 {res}      786  .4860051  .0178388  1.029048
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat experiment2outcomecorruption, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:experiment~n} {...}
{c |}{...}
 {res}      786  .2989822    .01634   1.53221
{txt}{hline 13}{c BT}{hline 40}

{com}. 
. ***********
. *Figure A2*
. ***********
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *Post Only (Produces values for the left three bars in Figure A2)
. tabstat betweensubjectsdv if drug_control==1, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:betweensub~v} {...}
{c |}{...}
 {res}      373  .9517426  .0111114  .2254784
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat betweensubjectsdv if drug_saveonly==1, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:betweensub~v} {...}
{c |}{...}
 {res}      404  .9282178  .0128582  .2784335
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat betweensubjectsdv if drug_limitonly==1, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:betweensub~v} {...}
{c |}{...}
 {res}      430  .7209302  .0216558  .6228957
{txt}{hline 13}{c BT}{hline 40}

{com}. 
. *RMWS (Produces values for the right three bars in Figure A2)
. tabstat within_initial, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:within_ini~l} {...}
{c |}{...}
 {res}      794  .9345088  .0087851  .2648948
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat withinsubjectssaveoutcome, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:wit~eoutcome} {...}
{c |}{...}
 {res}      795  .9119497  .0100564  .3109233
{txt}{hline 13}{c BT}{hline 40}

{com}. tabstat withinsubjectslimitoutcome, statistics(n mean semean cv)

{txt}{ralign 12:variable} {...}
{c |}         N      mean  se(mean)        cv
{hline 13}{c +}{hline 40}
{ralign 12:wit~toutcome} {...}
{c |}{...}
 {res}      795  .4226415  .0175307  1.169526
{txt}{hline 13}{c BT}{hline 40}

{com}. 
. ***********
. *Table A3a*
. ***********
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. *Baseline-Taxes-Corruption Order
. *Line 130 produces the value of the first row, first column of Table A3a
. sum experiment2outcometaxes if dc_taxcorr==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
experiment~s {c |}{res}        394    .4898477    .5005325          0          1
{txt}
{com}. *Line 132 produces the value of the second row, second column of Table A3a
. sum experiment2outcomecorruption if dc_taxcorr==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
experiment~n {c |}{res}        394    .3147208    .4649951          0          1
{txt}
{com}. 
. *Baseline-Corruption-Taxes
. *Line 136 produces the value of the first row, second column of Table A3a
. sum experiment2outcometaxes if dc_corrtax==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
experiment~s {c |}{res}        392    .4821429    .5003196          0          1
{txt}
{com}. *Line 138 produces the value of the second row, first column of Table A3a
. sum experiment2outcomecorruption if dc_corrtax==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
experiment~n {c |}{res}        392    .2831633    .4511108          0          1
{txt}
{com}. 
. ***********
. *Table A3b*
. ***********
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *Baseline-Save-Limit
. *Line 148 produces the value of the first row, first column of Table A3b
. sum withinsubjectssaveoutcome if drug_savetolimit==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
wit~eoutcome {c |}{res}        408    .9117647     .283985          0          1
{txt}
{com}. *Line 150 produces the value of the second row, second column of Table A3b
. sum withinsubjectslimitoutcome if drug_savetolimit==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
wit~toutcome {c |}{res}        408    .4534314    .4984378          0          1
{txt}
{com}. 
. *Baseline-Limit-Save
. *Line 154 produces the value of the first row, second column of Table A3b
. sum withinsubjectssaveoutcome if drug_limittosave==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
wit~eoutcome {c |}{res}        387    .9121447    .2834508          0          1
{txt}
{com}. *Line 156 produces the value of the second row, first column of Table A3b
. sum withinsubjectslimitoutcome if drug_limittosave==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
wit~toutcome {c |}{res}        387    .3901809    .4884221          0          1
{txt}
{com}. 
. ********************
. ******TABLE A4a*****
. ********************
. 
. *Study 1: Statistical Significance (Repeated Measures)
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. *First for effect of taxes argument
. 
. *Repeated Measures Significance Test - Taxes Argument
. *Lines 170-198 produce the values for the first column of Table A4a
. 
. *1. Drop those in Corruption Condition and then recode for four measurements (Exp. 1 Control DV, Exp. 1 Treat DV, Exp. 2 Baseline DV, Exp. 2 Treat DV)
. 
. drop if dc_corruptiononly==1
{txt}(391 observations deleted)

{com}. drop experiment2
{txt}
{com}. 
. gen dv1=experiment1outcome if dc_control==1
{txt}(1,178 missing values generated)

{com}. gen dv2=experiment2outcomebaseline
{txt}(792 missing values generated)

{com}. gen dv3=experiment1outcome if dc_taxesonly==1
{txt}(1,186 missing values generated)

{com}. gen dv4=experiment2outcometaxes
{txt}(792 missing values generated)

{com}. 
. sum dv1 dv2 dv3 dv4

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}dv1 {c |}{res}        400       .3875    .4877895          0          1
{txt}{space 9}dv2 {c |}{res}        786    .3905852    .4881922          0          1
{txt}{space 9}dv3 {c |}{res}        392    .4438776    .4974752          0          1
{txt}{space 9}dv4 {c |}{res}        786    .4860051    .5001223          0          1
{txt}
{com}. 
. *2. Generate unique identifier
. 
. generate id=_n
{txt}
{com}. 
. *3. Reshaping to long form
. 
. reshape long dv, i(id) j(time)
{txt}(note: j = 1 2 3 4)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1578   {txt}->{res}    6312
{txt}Number of variables            {res}      83   {txt}->{res}      81
{txt}j variable (4 values)                     ->   {res}time
{txt}xij variables:
                        {res}dv1 dv2 ... dv4   {txt}->   {res}dv
{txt}{hline 77}

{com}. gen taxestreat=0
{txt}
{com}. replace taxestreat=1 if time==3 | time==4
{txt}(3,156 real changes made)

{com}. gen experiment2=0
{txt}
{com}. replace experiment2=1 if time==2 | time==4
{txt}(3,156 real changes made)

{com}. 
. *4. Using the xtreg repeated measures
. 
. xtset id
{txt}{col 8}panel variable:  {res}id (balanced)
{txt}
{com}. xtreg dv i.taxestreat##i.experiment2, vce(cluster id) re
{res}
{txt}Random-effects GLS regression                   Number of obs     = {res}     2,364
{txt}Group variable: {res}id                              {txt}Number of groups  = {res}     1,578

{txt}R-sq:                                           Obs per group:
     within  = {res}0.0450                                         {txt}min = {res}         1
{txt}     between = {res}0.0024                                         {txt}avg = {res}       1.5
{txt}     overall = {res}0.0077                                         {txt}max = {res}         2

                                                {txt}Wald chi2({res}3{txt})      =  {res}    40.33
{txt}corr(u_i, X)   = {res}0{txt} (assumed)                    Prob > chi2       =     {res}0.0000

{txt}{ralign 88:(Std. Err. adjusted for {res:1,578} clusters in id)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                    dv{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.taxestreat {c |}{col 24}{res}{space 2} .0563776{col 36}{space 2} .0350059{col 47}{space 1}    1.61{col 56}{space 3}0.107{col 64}{space 4}-.0122327{col 77}{space 3} .1249878
{txt}{space 9}1.experiment2 {c |}{col 24}{res}{space 2} .0030852{col 36}{space 2}  .029965{col 47}{space 1}    0.10{col 56}{space 3}0.918{col 64}{space 4}-.0556451{col 77}{space 3} .0618156
{txt}{space 22} {c |}
taxestreat#experiment2 {c |}
{space 18}1 1  {c |}{col 24}{res}{space 2} .0390423{col 36}{space 2} .0383622{col 47}{space 1}    1.02{col 56}{space 3}0.309{col 64}{space 4}-.0361463{col 77}{space 3} .1142309
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}    .3875{col 36}{space 2} .0243822{col 47}{space 1}   15.89{col 56}{space 3}0.000{col 64}{space 4} .3397118{col 77}{space 3} .4352882
{txt}{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               sigma_u {c |} {res} .38302887
               {txt}sigma_e {c |} {res} .31099134
                   {txt}rho {c |} {res} .60269107{txt}   (fraction of variance due to u_i)
{hline 23}{c BT}{hline 64}

{com}. 
. *Repeated Measures Significance Test - Corruption Argument
. *Lines 206-233 produce the values for the second column of Table A4a
. 
. clear all
{res}{txt}
{com}. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. *1. Drop those in Corruption Condition and then recode for four measurements (Exp. 1 Control DV, Exp. 1 Treat DV, Exp. 2 Baseline DV, Exp. 2 Treat DV)
. 
. drop if dc_taxesonly==1
{txt}(392 observations deleted)

{com}. drop experiment2
{txt}
{com}. 
. gen dv1=experiment1outcome if dc_control==1
{txt}(1,177 missing values generated)

{com}. gen dv2=experiment2outcomebaseline
{txt}(791 missing values generated)

{com}. gen dv3=experiment1outcome if dc_corruptiononly==1
{txt}(1,186 missing values generated)

{com}. gen dv4=experiment2outcomecorruption
{txt}(791 missing values generated)

{com}. 
. sum dv1 dv2 dv3 dv4

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}dv1 {c |}{res}        400       .3875    .4877895          0          1
{txt}{space 9}dv2 {c |}{res}        786    .3905852    .4881922          0          1
{txt}{space 9}dv3 {c |}{res}        391    .3657289     .482251          0          1
{txt}{space 9}dv4 {c |}{res}        786    .2989822    .4581035          0          1
{txt}
{com}. 
. *2. Generate unique identifier
. 
. generate id=_n
{txt}
{com}. 
. *3. Reshaping to long form
. 
. reshape long dv, i(id) j(time)
{txt}(note: j = 1 2 3 4)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1577   {txt}->{res}    6308
{txt}Number of variables            {res}      83   {txt}->{res}      81
{txt}j variable (4 values)                     ->   {res}time
{txt}xij variables:
                        {res}dv1 dv2 ... dv4   {txt}->   {res}dv
{txt}{hline 77}

{com}. gen corruptiontreat=0
{txt}
{com}. replace corruptiontreat=1 if time==3 | time==4
{txt}(3,154 real changes made)

{com}. gen experiment2=0
{txt}
{com}. replace experiment2=1 if time==2 | time==4
{txt}(3,154 real changes made)

{com}. 
. *4. Using the xtreg repeated measures
. 
. xtset id
{txt}{col 8}panel variable:  {res}id (balanced)
{txt}
{com}. xtreg dv i.corruptiontreat##i.experiment2, vce(cluster id) re
{res}
{txt}Random-effects GLS regression                   Number of obs     = {res}     2,363
{txt}Group variable: {res}id                              {txt}Number of groups  = {res}     1,577

{txt}R-sq:                                           Obs per group:
     within  = {res}0.0397                                         {txt}min = {res}         1
{txt}     between = {res}0.0015                                         {txt}avg = {res}       1.5
{txt}     overall = {res}0.0073                                         {txt}max = {res}         2

                                                {txt}Wald chi2({res}3{txt})      =  {res}    35.69
{txt}corr(u_i, X)   = {res}0{txt} (assumed)                    Prob > chi2       =     {res}0.0000

{txt}{ralign 93:(Std. Err. adjusted for {res:1,577} clusters in id)}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                         dv{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.corruptiontreat {c |}{col 29}{res}{space 2}-.0217711{col 41}{space 2} .0344804{col 52}{space 1}   -0.63{col 61}{space 3}0.528{col 69}{space 4}-.0893515{col 82}{space 3} .0458093
{txt}{space 14}1.experiment2 {c |}{col 29}{res}{space 2} .0030852{col 41}{space 2}  .029965{col 52}{space 1}    0.10{col 61}{space 3}0.918{col 69}{space 4}-.0556452{col 82}{space 3} .0618156
{txt}{space 27} {c |}
corruptiontreat#experiment2 {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} -.069832{col 41}{space 2} .0380449{col 52}{space 1}   -1.84{col 61}{space 3}0.066{col 69}{space 4}-.1443985{col 82}{space 3} .0047346
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}    .3875{col 41}{space 2} .0243822{col 52}{space 1}   15.89{col 61}{space 3}0.000{col 69}{space 4} .3397118{col 82}{space 3} .4352882
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                    sigma_u {c |} {res}  .3579885
                    {txt}sigma_e {c |} {res}  .3186402
                        {txt}rho {c |} {res} .55795745{txt}   (fraction of variance due to u_i)
{hline 28}{c BT}{hline 64}

{com}. 
. clear all
{res}{txt}
{com}. 
. ********************
. ******TABLE A4b*****
. ********************
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *Study 2: Statistical Significance (Repeated Measures)
. 
. *First for effect of taxes argument
. 
. *NOTE: I do not save the stacked datasets for version control
. 
. *Repeated Measures Significance Test - Save Money
. *Lines 252-278 produce the values for the first column of Table A4b
. 
. *1. Drop those in Limit Access conditions and then recode for four measurements (Exp. 1 Control DV, Exp. 1 Treat DV, Exp. 2 Baseline DV, Exp. 2 Treat DV)
. 
. drop if drug_limitonly==1
{txt}(444 observations deleted)

{com}. 
. gen dv1=betweensubjectsdv if drug_control==1
{txt}(1,228 missing values generated)

{com}. gen dv2=within_initial
{txt}(807 missing values generated)

{com}. gen dv3=betweensubjectsdv if drug_saveonly==1
{txt}(1,197 missing values generated)

{com}. gen dv4=withinsubjectssaveoutcome
{txt}(806 missing values generated)

{com}. 
. sum dv1 dv2 dv3 dv4

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}dv1 {c |}{res}        373    .9517426    .2145974          0          1
{txt}{space 9}dv2 {c |}{res}        794    .9345088    .2475465          0          1
{txt}{space 9}dv3 {c |}{res}        404    .9282178     .258447          0          1
{txt}{space 9}dv4 {c |}{res}        795    .9119497    .2835464          0          1
{txt}
{com}. 
. *2. Generate unique identifier
. 
. generate id=_n
{txt}
{com}. 
. *3. Reshaping to long form
. 
. reshape long dv, i(id) j(time)
{txt}(note: j = 1 2 3 4)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1601   {txt}->{res}    6404
{txt}Number of variables            {res}      67   {txt}->{res}      65
{txt}j variable (4 values)                     ->   {res}time
{txt}xij variables:
                        {res}dv1 dv2 ... dv4   {txt}->   {res}dv
{txt}{hline 77}

{com}. gen savetreat=0
{txt}
{com}. replace savetreat=1 if time==3 | time==4
{txt}(3,202 real changes made)

{com}. gen experiment2=0
{txt}
{com}. replace experiment2=1 if time==2 | time==4
{txt}(3,202 real changes made)

{com}. 
. *4. Using the xtreg repeated measures
. 
. xtset id
{txt}{col 8}panel variable:  {res}id (balanced)
{txt}
{com}. xtreg dv i.savetreat##i.experiment2, vce(cluster id) re
{res}
{txt}Random-effects GLS regression                   Number of obs     = {res}     2,366
{txt}Group variable: {res}id                              {txt}Number of groups  = {res}     1,572

{txt}R-sq:                                           Obs per group:
     within  = {res}0.0057                                         {txt}min = {res}         1
{txt}     between = {res}0.0025                                         {txt}avg = {res}       1.5
{txt}     overall = {res}0.0029                                         {txt}max = {res}         2

                                                {txt}Wald chi2({res}3{txt})      =  {res}     8.39
{txt}corr(u_i, X)   = {res}0{txt} (assumed)                    Prob > chi2       =     {res}0.0386

{txt}{ralign 87:(Std. Err. adjusted for {res:1,572} clusters in id)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}                   dv{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.savetreat {c |}{col 23}{res}{space 2}-.0235248{col 35}{space 2} .0169884{col 46}{space 1}   -1.38{col 55}{space 3}0.166{col 63}{space 4}-.0568215{col 76}{space 3} .0097719
{txt}{space 8}1.experiment2 {c |}{col 23}{res}{space 2}-.0172012{col 35}{space 2} .0141624{col 46}{space 1}   -1.21{col 55}{space 3}0.225{col 63}{space 4} -.044959{col 76}{space 3} .0105565
{txt}{space 21} {c |}
savetreat#experiment2 {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2} .0009331{col 35}{space 2} .0200566{col 46}{space 1}    0.05{col 55}{space 3}0.963{col 63}{space 4}-.0383771{col 76}{space 3} .0402433
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .9517426{col 35}{space 2} .0111071{col 46}{space 1}   85.69{col 55}{space 3}0.000{col 63}{space 4} .9299731{col 76}{space 3} .9735122
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              sigma_u {c |} {res} .13706755
              {txt}sigma_e {c |} {res} .21246161
                  {txt}rho {c |} {res} .29388805{txt}   (fraction of variance due to u_i)
{hline 22}{c BT}{hline 64}

{com}. 
. *Repeated Measures Significance Test - Limit Access
. *Lines 288-314 produce the values for the second column of Table A4b
. 
. *Bring Data back in
. 
. clear all
{res}{txt}
{com}. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *1. Drop those in Save Money conditions and then recode for four measurements (Exp. 1 Control DV, Exp. 1 Treat DV, Exp. 2 Baseline DV, Exp. 2 Treat DV)
. 
. drop if drug_saveonly==1
{txt}(410 observations deleted)

{com}. 
. gen dv1=betweensubjectsdv if drug_control==1
{txt}(1,262 missing values generated)

{com}. gen dv2=within_initial
{txt}(841 missing values generated)

{com}. gen dv3=betweensubjectsdv if drug_limitonly==1
{txt}(1,205 missing values generated)

{com}. gen dv4=withinsubjectslimitoutcome
{txt}(840 missing values generated)

{com}. 
. sum dv1 dv2 dv3 dv4

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}dv1 {c |}{res}        373    .9517426    .2145974          0          1
{txt}{space 9}dv2 {c |}{res}        794    .9345088    .2475465          0          1
{txt}{space 9}dv3 {c |}{res}        430    .7209302    .4490644          0          1
{txt}{space 9}dv4 {c |}{res}        795    .4226415    .4942904          0          1
{txt}
{com}. 
. *2. Generate unique identifier
. 
. generate id=_n
{txt}
{com}. 
. *3. Reshaping to long form
. 
. reshape long dv, i(id) j(time)
{txt}(note: j = 1 2 3 4)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1635   {txt}->{res}    6540
{txt}Number of variables            {res}      67   {txt}->{res}      65
{txt}j variable (4 values)                     ->   {res}time
{txt}xij variables:
                        {res}dv1 dv2 ... dv4   {txt}->   {res}dv
{txt}{hline 77}

{com}. gen limittreat=0
{txt}
{com}. replace limittreat=1 if time==3 | time==4
{txt}(3,270 real changes made)

{com}. gen experiment2=0
{txt}
{com}. replace experiment2=1 if time==2 | time==4
{txt}(3,270 real changes made)

{com}. 
. *4. Using the xtreg repeated measures
. 
. xtset id
{txt}{col 8}panel variable:  {res}id (balanced)
{txt}
{com}. xtreg dv i.limittreat##i.experiment2, vce(cluster id) re
{res}
{txt}Random-effects GLS regression                   Number of obs     = {res}     2,392
{txt}Group variable: {res}id                              {txt}Number of groups  = {res}     1,598

{txt}R-sq:                                           Obs per group:
     within  = {res}0.4841                                         {txt}min = {res}         1
{txt}     between = {res}0.1024                                         {txt}avg = {res}       1.5
{txt}     overall = {res}0.2679                                         {txt}max = {res}         2

                                                {txt}Wald chi2({res}3{txt})      =  {res}   852.45
{txt}corr(u_i, X)   = {res}0{txt} (assumed)                    Prob > chi2       =     {res}0.0000

{txt}{ralign 88:(Std. Err. adjusted for {res:1,598} clusters in id)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                    dv{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.limittreat {c |}{col 24}{res}{space 2}-.2308124{col 36}{space 2} .0243337{col 47}{space 1}   -9.49{col 56}{space 3}0.000{col 64}{space 4}-.2785056{col 77}{space 3}-.1831192
{txt}{space 9}1.experiment2 {c |}{col 24}{res}{space 2}-.0172299{col 36}{space 2}  .014163{col 47}{space 1}   -1.22{col 56}{space 3}0.224{col 64}{space 4}-.0449889{col 77}{space 3} .0105291
{txt}{space 22} {c |}
limittreat#experiment2 {c |}
{space 18}1 1  {c |}{col 24}{res}{space 2}-.2810588{col 36}{space 2}  .030746{col 47}{space 1}   -9.14{col 56}{space 3}0.000{col 64}{space 4}-.3413199{col 77}{space 3}-.2207977
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2} .9517426{col 36}{space 2}  .011107{col 47}{space 1}   85.69{col 56}{space 3}0.000{col 64}{space 4} .9299734{col 77}{space 3} .9735119
{txt}{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               sigma_u {c |} {res} .02749017
               {txt}sigma_e {c |} {res}  .3744471
                   {txt}rho {c |} {res} .00536092{txt}   (fraction of variance due to u_i)
{hline 23}{c BT}{hline 64}

{com}. 
. clear all
{res}{txt}
{com}. 
. ************
. **TABLE A5**
. ************
. 
. *Study 1 - Taxes
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 1.dta"
{txt}
{com}. 
. *Post Only vs. Control
. *Line 327 provides the values for the first row, first column of Table A5
. reg experiment1outcome dc_taxesonly if dc_corruptiononly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       792
{txt}{hline 13}{c +}{hline 34}   F(1, 790)       = {res}     2.59
{txt}       Model {c |} {res} .629264585         1  .629264585   {txt}Prob > F        ={res}    0.1077
{txt}    Residual {c |} {res} 191.702806       790   .24266178   {txt}R-squared       ={res}    0.0033
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0020
{txt}       Total {c |} {res} 192.332071       791  .243150532   {txt}Root MSE        =   {res} .49261

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}experimen~me{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dc_taxesonly {c |}{col 14}{res}{space 2} .0563776{col 26}{space 2} .0350099{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0123458{col 67}{space 3} .1251009
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}    .3875{col 26}{space 2} .0246304{col 37}{space 1}   15.73{col 46}{space 3}0.000{col 54}{space 4} .3391513{col 67}{space 3} .4358487
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *RMWS First Q vs. Control
. *Line 331 provides the values for the second row, first column of Table A5
. reg rmwstaxesvscontrol dc_taxcorr

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       794
{txt}{hline 13}{c +}{hline 34}   F(1, 792)       = {res}     8.51
{txt}       Model {c |} {res} 2.07917967         1  2.07917967   {txt}Prob > F        ={res}    0.0036
{txt}    Residual {c |} {res} 193.396891       792  .244187994   {txt}R-squared       ={res}    0.0106
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0094
{txt}       Total {c |} {res} 195.476071       793   .24650198   {txt}Root MSE        =   {res} .49415

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}rmwstaxesv~l{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}dc_taxcorr {c |}{col 14}{res}{space 2} .1023477{col 26}{space 2} .0350747{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0334973{col 67}{space 3} .1711981
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}    .3875{col 26}{space 2} .0247077{col 37}{space 1}   15.68{col 46}{space 3}0.000{col 54}{space 4} .3389997{col 67}{space 3} .4360003
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Diff-in-Diff
. *Line 335 provides a coefficient on the dc_taxcorr variable, which is the diff-in-diff
. reg taxesdiff dc_taxcorr dc_control

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,186
{txt}{hline 13}{c +}{hline 34}   F(2, 1183)      = {res}     4.26
{txt}       Model {c |} {res} 2.08738143         2  1.04369071   {txt}Prob > F        ={res}    0.0144
{txt}    Residual {c |} {res} 290.162197     1,183  .245276582   {txt}R-squared       ={res}    0.0071
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0055
{txt}       Total {c |} {res} 292.249578     1,185  .246624117   {txt}Root MSE        =   {res} .49525

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   taxesdiff{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}dc_taxcorr {c |}{col 14}{res}{space 2} .0459702{col 26}{space 2} .0353304{col 37}{space 1}    1.30{col 46}{space 3}0.193{col 54}{space 4} -.023347{col 67}{space 3} .1152873
{txt}{space 2}dc_control {c |}{col 14}{res}{space 2}-.0563776{col 26}{space 2}  .035198{col 37}{space 1}   -1.60{col 46}{space 3}0.109{col 54}{space 4} -.125435{col 67}{space 3} .0126799
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4438776{col 26}{space 2} .0250141{col 37}{space 1}   17.75{col 46}{space 3}0.000{col 54}{space 4} .3948006{col 67}{space 3} .4929545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Study 1 - Corruption
. *Line 340 provides the values for the first row, second column of Table A5
. *Post Only vs. Control
. reg experiment1outcome dc_corruptiononly if dc_taxesonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       791
{txt}{hline 13}{c +}{hline 34}   F(1, 789)       = {res}     0.40
{txt}       Model {c |} {res} .093717566         1  .093717566   {txt}Prob > F        ={res}    0.5281
{txt}    Residual {c |} {res} 185.638267       789  .235282975   {txt}R-squared       ={res}    0.0005
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0008
{txt}       Total {c |} {res} 185.731985       790  .235103778   {txt}Root MSE        =   {res} .48506

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}experiment1outc~e{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      t{col 51}   P>|t|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dc_corruptiononly {c |}{col 19}{res}{space 2}-.0217711{col 31}{space 2} .0344957{col 42}{space 1}   -0.63{col 51}{space 3}0.528{col 59}{space 4}-.0894853{col 72}{space 3} .0459431
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}    .3875{col 31}{space 2}  .024253{col 42}{space 1}   15.98{col 51}{space 3}0.000{col 59}{space 4}  .339892{col 72}{space 3}  .435108
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *RMWS First Q vs. Control
. *Line 344 provides the values for the second row, second column of Table A5
. reg rmwscorrvscontrol dc_corrtax

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       792
{txt}{hline 13}{c +}{hline 34}   F(1, 790)       = {res}     9.76
{txt}       Model {c |} {res} 2.15523861         1  2.15523861   {txt}Prob > F        ={res}    0.0019
{txt}    Residual {c |} {res} 174.506378       790  .220894149   {txt}R-squared       ={res}    0.0122
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0109
{txt}       Total {c |} {res} 176.661616       791  .223339591   {txt}Root MSE        =   {res} .46999

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}rmwscorrvs~l{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}dc_corrtax {c |}{col 14}{res}{space 2}-.1043367{col 26}{space 2} .0334027{col 37}{space 1}   -3.12{col 46}{space 3}0.002{col 54}{space 4}-.1699053{col 67}{space 3}-.0387682
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}    .3875{col 26}{space 2} .0234997{col 37}{space 1}   16.49{col 46}{space 3}0.000{col 54}{space 4} .3413708{col 67}{space 3} .4336292
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Diff-in-Diff
. *Line 348 provides a coefficient on the dc_corrtax variable, which is the diff-in-diff
. reg corrdiff dc_corrtax dc_control

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,183
{txt}{hline 13}{c +}{hline 34}   F(2, 1180)      = {res}     5.31
{txt}       Model {c |} {res}  2.3887977         2  1.19439885   {txt}Prob > F        ={res}    0.0050
{txt}    Residual {c |} {res} 265.207145     1,180  .224751818   {txt}R-squared       ={res}    0.0089
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0072
{txt}       Total {c |} {res} 267.595943     1,182  .226392506   {txt}Root MSE        =   {res} .47408

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    corrdiff{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}dc_corrtax {c |}{col 14}{res}{space 2}-.0825656{col 26}{space 2} .0338845{col 37}{space 1}   -2.44{col 46}{space 3}0.015{col 54}{space 4}-.1490462{col 67}{space 3} -.016085
{txt}{space 2}dc_control {c |}{col 14}{res}{space 2} .0217711{col 26}{space 2} .0337149{col 37}{space 1}    0.65{col 46}{space 3}0.519{col 54}{space 4}-.0443767{col 67}{space 3} .0879189
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3657289{col 26}{space 2} .0239753{col 37}{space 1}   15.25{col 46}{space 3}0.000{col 54}{space 4}   .31869{col 67}{space 3} .4127678
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *Study 2 - Save Money
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *Post Only vs. Control
. *Line 356 provides the values for the first row, third column of Table A5
. reg betweensubjectsdv drug_saveonly if drug_limitonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       777
{txt}{hline 13}{c +}{hline 34}   F(1, 775)       = {res}     1.89
{txt}       Model {c |} {res} .107330033         1  .107330033   {txt}Prob > F        ={res}    0.1698
{txt}    Residual {c |} {res} 44.0496841       775  .056838302   {txt}R-squared       ={res}    0.0024
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0011
{txt}       Total {c |} {res} 44.1570142       776  .056903369   {txt}Root MSE        =   {res} .23841

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubj~v{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_saveonly {c |}{col 15}{res}{space 2}-.0235248{col 27}{space 2} .0171193{col 38}{space 1}   -1.37{col 47}{space 3}0.170{col 55}{space 4}-.0571305{col 68}{space 3} .0100809
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9517426{col 27}{space 2} .0123443{col 38}{space 1}   77.10{col 47}{space 3}0.000{col 55}{space 4} .9275104{col 68}{space 3} .9759748
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *RMWS First Q vs. Control
. *Line 360 provides the values for the second row, third column of Table A5
. reg rmwssavevscontrol drug_savetolimit

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       781
{txt}{hline 13}{c +}{hline 34}   F(1, 779)       = {res}     4.86
{txt}       Model {c |} {res}  .31142852         1   .31142852   {txt}Prob > F        ={res}    0.0278
{txt}    Residual {c |} {res} 49.9548967       779  .064126953   {txt}R-squared       ={res}    0.0062
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0049
{txt}       Total {c |} {res} 50.2663252       780  .064444007   {txt}Root MSE        =   {res} .25323

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}rmwssavevscont~l{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_savetolimit {c |}{col 18}{res}{space 2}-.0399779{col 30}{space 2}  .018141{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4} -.075589{col 71}{space 3}-.0043669
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9517426{col 30}{space 2} .0131119{col 41}{space 1}   72.59{col 50}{space 3}0.000{col 58}{space 4} .9260038{col 71}{space 3} .9774815
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Diff-in-Diff
. *Line 364 provides a coefficient on the drug_savetolimit variable, which is the diff-in-diff
. reg savediff drug_savetolimit drug_control

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,185
{txt}{hline 13}{c +}{hline 34}   F(2, 1182)      = {res}     2.41
{txt}       Model {c |} {res} .313284355         2  .156642177   {txt}Prob > F        ={res}    0.0904
{txt}    Residual {c |} {res} 76.8732135     1,182   .06503656   {txt}R-squared       ={res}    0.0041
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0024
{txt}       Total {c |} {res} 77.1864979     1,184  .065191299   {txt}Root MSE        =   {res} .25502

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        savediff{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_savetolimit {c |}{col 18}{res}{space 2}-.0164531{col 30}{space 2} .0178993{col 41}{space 1}   -0.92{col 50}{space 3}0.358{col 58}{space 4}-.0515711{col 71}{space 3} .0186648
{txt}{space 4}drug_control {c |}{col 18}{res}{space 2} .0235248{col 30}{space 2} .0183124{col 41}{space 1}    1.28{col 50}{space 3}0.199{col 58}{space 4}-.0124035{col 71}{space 3} .0594532
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9282178{col 30}{space 2} .0126879{col 41}{space 1}   73.16{col 50}{space 3}0.000{col 58}{space 4} .9033246{col 71}{space 3}  .953111
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Study 2 - Limit Access
. 
. *Post Only vs. Control
. *Line 370 provides the values for the first row, fourth column of Table A5
. reg betweensubjectsdv drug_limitonly if drug_saveonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       803
{txt}{hline 13}{c +}{hline 34}   F(1, 801)       = {res}    82.24
{txt}       Model {c |} {res}   10.64094         1    10.64094   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 103.642995       801  .129392004   {txt}R-squared       ={res}    0.0931
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0920
{txt}       Total {c |} {res} 114.283935       802  .142498672   {txt}Root MSE        =   {res} .35971

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2308124{col 28}{space 2} .0254521{col 39}{space 1}   -9.07{col 48}{space 3}0.000{col 56}{space 4} -.280773{col 69}{space 3}-.1808518
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9517426{col 28}{space 2} .0186251{col 39}{space 1}   51.10{col 48}{space 3}0.000{col 56}{space 4} .9151828{col 69}{space 3} .9883025
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *RMWS First Q vs. Control
. *Line 374 provides the values for the first row, third column of Table A5
. reg rmwslimitvscontrol drug_limittosave

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       760
{txt}{hline 13}{c +}{hline 34}   F(1, 758)       = {res}   415.71
{txt}       Model {c |} {res} 59.8964717         1  59.8964717   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 109.214055       758  .144081866   {txt}R-squared       ={res}    0.3542
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3533
{txt}       Total {c |} {res} 169.110526       759  .222807018   {txt}Root MSE        =   {res} .37958

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}rmwslimitvscon~l{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limittosave {c |}{col 18}{res}{space 2}-.5615617{col 30}{space 2} .0275424{col 41}{space 1}  -20.39{col 50}{space 3}0.000{col 58}{space 4}-.6156302{col 71}{space 3}-.5074933
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9517426{col 30}{space 2}  .019654{col 41}{space 1}   48.42{col 50}{space 3}0.000{col 58}{space 4}   .91316{col 71}{space 3} .9903253
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Diff-in-Diff
. *Line 378 provides a coefficient on the drug_limittosave variable, which is the diff-in-diff
. reg limitdiff drug_limittosave drug_control

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,190
{txt}{hline 13}{c +}{hline 34}   F(2, 1187)      = {res}   184.16
{txt}       Model {c |} {res} 60.7314603         2  30.3657302   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 195.725683     1,187  .164891055   {txt}R-squared       ={res}    0.2368
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2355
{txt}       Total {c |} {res} 256.457143     1,189  .215691457   {txt}Root MSE        =   {res} .40607

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       limitdiff{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      t{col 50}   P>|t|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limittosave {c |}{col 18}{res}{space 2}-.3307494{col 30}{space 2} .0284525{col 41}{space 1}  -11.62{col 50}{space 3}0.000{col 58}{space 4}-.3865721{col 71}{space 3}-.2749266
{txt}{space 4}drug_control {c |}{col 18}{res}{space 2} .2308124{col 30}{space 2} .0287321{col 41}{space 1}    8.03{col 50}{space 3}0.000{col 58}{space 4}  .174441{col 71}{space 3} .2871838
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .7209302{col 30}{space 2} .0195823{col 41}{space 1}   36.82{col 50}{space 3}0.000{col 58}{space 4} .6825104{col 71}{space 3} .7593501
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ************
. **TABLE A6**
. ************
. 
. use "C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\Study 2.dta"
{txt}
{com}. 
. *Lines 388-389 produce values for the first column of Table A6
. reg betweensubjectsdv drug_limitonly if drug_saveonly!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       803
{txt}{hline 13}{c +}{hline 34}   F(1, 801)       = {res}    82.24
{txt}       Model {c |} {res}   10.64094         1    10.64094   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 103.642995       801  .129392004   {txt}R-squared       ={res}    0.0931
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0920
{txt}       Total {c |} {res} 114.283935       802  .142498672   {txt}Root MSE        =   {res} .35971

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2308124{col 28}{space 2} .0254521{col 39}{space 1}   -9.07{col 48}{space 3}0.000{col 56}{space 4} -.280773{col 69}{space 3}-.1808518
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9517426{col 28}{space 2} .0186251{col 39}{space 1}   51.10{col 48}{space 3}0.000{col 56}{space 4} .9151828{col 69}{space 3} .9883025
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. bysort foldedideo: reg betweensubjectsdv drug_limitonly if drug_saveonly!=1

{txt}{hline}
-> foldedideo = Moderate

      Source {c |}       SS           df       MS      Number of obs   ={res}       363
{txt}{hline 13}{c +}{hline 34}   F(1, 361)       = {res}    35.49
{txt}       Model {c |} {res} 4.89038537         1  4.89038537   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 49.7432234       361  .137792863   {txt}R-squared       ={res}    0.0895
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0870
{txt}       Total {c |} {res} 54.6336088       362  .150921571   {txt}Root MSE        =   {res}  .3712

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2327839{col 28}{space 2} .0390746{col 39}{space 1}   -5.96{col 48}{space 3}0.000{col 56}{space 4}-.3096263{col 69}{space 3}-.1559414
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9404762{col 28}{space 2} .0286391{col 39}{space 1}   32.84{col 48}{space 3}0.000{col 56}{space 4} .8841558{col 69}{space 3} .9967965
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline}
-> foldedideo = Lean

      Source {c |}       SS           df       MS      Number of obs   ={res}       138
{txt}{hline 13}{c +}{hline 34}   F(1, 136)       = {res}    12.13
{txt}       Model {c |} {res} 1.72793484         1  1.72793484   {txt}Prob > F        ={res}    0.0007
{txt}    Residual {c |} {res} 19.3735144       136  .142452312   {txt}R-squared       ={res}    0.0819
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0751
{txt}       Total {c |} {res} 21.1014493       137  .154025177   {txt}Root MSE        =   {res} .37743

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2249576{col 28}{space 2} .0645909{col 39}{space 1}   -3.48{col 48}{space 3}0.001{col 56}{space 4}-.3526901{col 69}{space 3} -.097225
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9354839{col 28}{space 2} .0479335{col 39}{space 1}   19.52{col 48}{space 3}0.000{col 56}{space 4} .8406925{col 69}{space 3} 1.030275
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline}
-> foldedideo = Lib/Con

      Source {c |}       SS           df       MS      Number of obs   ={res}       124
{txt}{hline 13}{c +}{hline 34}   F(1, 122)       = {res}    17.22
{txt}       Model {c |} {res} 1.81451613         1  1.81451613   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 12.8548387       122   .10536753   {txt}R-squared       ={res}    0.1237
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1165
{txt}       Total {c |} {res} 14.6693548       123  .119263047   {txt}Root MSE        =   {res}  .3246

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2419355{col 28}{space 2} .0583005{col 39}{space 1}   -4.15{col 48}{space 3}0.000{col 56}{space 4}-.3573472{col 69}{space 3}-.1265237
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  .983871{col 28}{space 2} .0412247{col 39}{space 1}   23.87{col 48}{space 3}0.000{col 56}{space 4} .9022625{col 69}{space 3} 1.065479
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline}
-> foldedideo = Strong Lib/Con

      Source {c |}       SS           df       MS      Number of obs   ={res}       178
{txt}{hline 13}{c +}{hline 34}   F(1, 176)       = {res}    17.64
{txt}       Model {c |} {res}  2.1499157         1   2.1499157   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 21.4455899       176  .121849943   {txt}R-squared       ={res}    0.0911
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0860
{txt}       Total {c |} {res} 23.5955056       177  .133307941   {txt}Root MSE        =   {res} .34907

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}betweensubje~v{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
drug_limitonly {c |}{col 16}{res}{space 2}-.2206949{col 28}{space 2} .0525405{col 39}{space 1}   -4.20{col 48}{space 3}0.000{col 56}{space 4}-.3243854{col 69}{space 3}-.1170044
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  .962963{col 28}{space 2} .0387856{col 39}{space 1}   24.83{col 48}{space 3}0.000{col 56}{space 4} .8864183{col 69}{space 3} 1.039508
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline}
-> foldedideo = .
no observations

{com}. 
. *Lines 392-393 produce values for the second column of Table A6
. ttest within_initial=withinsubjectslimitoutcome

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    794{col 22} .9345088{col 34} .0087851{col 46} .2475465{col 58}  .917264{col 70} .9517536
{txt}w~limi~e {c |}{res}{col 12}    794{col 22} .4219144{col 34} .0175377{col 46} .4941763{col 58} .3874886{col 70} .4563401
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    794{col 22} .5125945{col 34}  .018793{col 46} .5295482{col 58} .4757046{col 70} .5494843
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~toutcome{txt})         t = {res} 27.2759
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     793

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. bysort foldedideo: ttest within_initial=withinsubjectslimitoutcome

{txt}{hline}
-> foldedideo = Moderate

Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    318{col 22} .9213836{col 34} .0151164{col 46} .2695632{col 58} .8916426{col 70} .9511247
{txt}w~limi~e {c |}{res}{col 12}    318{col 22} .3836478{col 34} .0273119{col 46} .4870401{col 58} .3299124{col 70} .4373832
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    318{col 22} .5377358{col 34} .0297212{col 46} .5300056{col 58}   .47926{col 70} .5962117
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~toutcome{txt})         t = {res} 18.0926
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     317

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000

{txt}{hline}
-> foldedideo = Lean

Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    142{col 22}  .943662{col 34} .0194178{col 46} .2313895{col 58} .9052744{col 70} .9820496
{txt}w~limi~e {c |}{res}{col 12}    142{col 22} .3450704{col 34} .0400352{col 46}  .477074{col 58} .2659236{col 70} .4242172
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    142{col 22} .5985915{col 34} .0436336{col 46} .5199544{col 58} .5123309{col 70} .6848522
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~toutcome{txt})         t = {res} 13.7186
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     141

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000

{txt}{hline}
-> foldedideo = Lib/Con

Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    146{col 22} .9452055{col 34} .0188994{col 46} .2283621{col 58} .9078516{col 70} .9825594
{txt}w~limi~e {c |}{res}{col 12}    146{col 22} .4041096{col 34}  .040752{col 46} .4924081{col 58} .3235649{col 70} .4846542
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    146{col 22} .5410959{col 34} .0413822{col 46} .5000236{col 58} .4593055{col 70} .6228862
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~toutcome{txt})         t = {res} 13.0756
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     145

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000

{txt}{hline}
-> foldedideo = Strong Lib/Con

Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
within~l {c |}{res}{col 12}    188{col 22} .9414894{col 34} .0171634{col 46} .2353332{col 58} .9076305{col 70} .9753482
{txt}w~limi~e {c |}{res}{col 12}    188{col 22} .5585106{col 34} .0363124{col 46} .4978906{col 58}  .486876{col 70} .6301453
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    188{col 22} .3829787{col 34} .0393461{col 46} .5394864{col 58} .3053595{col 70}  .460598
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}within_initial{txt} - {res}withins~toutcome{txt})         t = {res}  9.7336
{txt} Ho: mean(diff) = 0                              degrees of freedom = {res}     187

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000

{txt}{hline}
-> foldedideo = .
no observations

{com}. 
. 
. **********
. *Table A7*
. **********
. 
. ta withinsubjects finaloutcome, row missing
{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

withinsubj {c |}           finaloutcome
      ects {c |}         0          1          . {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}       167      1,040         26 {txt}{c |}{res}     1,233 
           {txt}{c |}{res}     13.54      84.35       2.11 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
         1 {c |}{res}       257        538         17 {txt}{c |}{res}       812 
           {txt}{c |}{res}     31.65      66.26       2.09 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}       424      1,578         43 {txt}{c |}{res}     2,045 
           {txt}{c |}{res}     20.73      77.16       2.10 {txt}{c |}{res}    100.00 

{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jared McDonald\Dropbox\Policy Rationale Project\Replication Files\PSRM McDonald Hanmer.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Aug 2023, 19:05:20
{txt}{.-}
{smcl}
{txt}{sf}{ul off}